We would like to thank V. Kapoor in the CCR ETIB Flow Cytometry Core for her aid in performing the cell sorting during the development of this protocol. We also thank M. Raffeld and the CCR LP Molecular Diagnostics Unit and J. Zhu and the NHLBI DNA Sequencing and Genomics Core for their aid in performing the qPCR during the development of this protocol. This research was supported by the Intramural Program of the NIH.

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The authors have nothing to disclose.

We have presented a method for isolating individual mammalian cells from a population of adherent cells grown in culture and for assaying the expression of approximately 96 genes in each cell. Good advance preparation is critical for this method to work well. In particular, designing and testing primer pairs specific to the transcripts of interest (steps 1.2-1.3) are time-consuming but important steps, as the primers determine the quality of the single-cell measurements. Once reliable primer pairs have been obtained, they are used to amplify cDNA from the transcripts of interest; the amplicons are then combined together in equimolar amounts to make standards (step 1.4). This step is critical, as the standards are required to estimate the absolute transcript counts; standards should be made once, aliquoted, and used for all subsequent rounds of cell sorting and gene expression measurements. Likewise, on the day that the cells are sorted, it is especially important to dilute the standards carefully using low-binding pipette tips and microcentrifuge tubes when making plates of lysis buffer (steps 3.7 and 3.11). Aiming the cell sorter (step 4.2) is another critical step; this must be done very carefully in order for the cells to successfully hit the target of 9 &#181;l of lysis buffer in a PCR plate.
There are several modifications to the protocol that can be made to adapt it to a variety of research needs. Here, we focused on a DNA binding dye-based qPCR approach, which provides a relatively affordable method for quantifying gene expression. However, this method has the potential for creating a high background signal, since any amplified DNA, even that of off-target sequences, results in a fluorescent signal. Such a background can be minimized by ensuring that the primer pair used to amplify a specific target yields a melt curve with a single peak and a single PCR product of the correct size (Figure 2). If the background is still a concern after using such quality-control methods, a probe-based qPCR approach can be used to minimize off-target detection, albeit at a greater cost of reagents. Another option would be a nested PCR approach, in which one set of primers is used in the RTSTA step to reverse-transcribe and amplify a large region of the transcript and a second set of primers, which amplifies a smaller region contained within that large region, is used to measure gene expression by qPCR. This approach has been shown to improve the specificity of DNA binding dye-based qPCR23.
Several methods are available for isolating individual cells for gene expression analysis. The choice of cell isolation method depends on several factors, including the availability of equipment (such as a fluorescence-activated cell sorter), the source of the cells to be evaluated (such as established cell lines versus primary cells from a tissue), or the speed with which the cells must be isolated. In the example presented in this protocol, we used FACS of a cell line expressing a clearly detectable fluorescent-tagged protein. FACS has the benefits of rare cell detection capabilities and relatively rapid cell isolation. One challenge of the method is that the deposition of the cells of interest into the requisite small volume of lysis buffer in a 96-well PCR plate can be inefficient and may require the optimization of the cell sorting geometry to ensure a successful isolation. Alternative approaches that may lead to greater precision in cell isolation at lower speeds and/or higher costs include the micropipetting of individual cells, the laser capture microdissection of specific cells from a tumor sample, or the use of microfluidic systems (e.g., the Fluidigm C1 system).
One major benefit of the protocol described here is that the internal controls, a dilution series of purified PCR amplicons, enable the estimation of the absolute number of each transcript in each cell. Without these standards, only relative levels of gene expression can be obtained based on calculations derived from the Ct values. However, with these standards, absolute transcript counts can be estimated, ideally through interpolation within the range of precisely quantified amplicon standards (Figure 8). As with any PCR-based method of gene expression quantification, accurate absolute quantification requires knowledge of the efficiency of each process, including reverse transcription.
A current challenge in quantifying transcript levels in single cells is that the limit of detection for many methods is estimated to be 10-100 mRNAs per cell24, preventing the reliable quantification of a significant percentage of the transcriptome. As an alternative approach, one can use smRNA FISH to quantify targets of particular interest25,26, including those with very few transcripts. Advances in smRNA FISH have expanded the number of distinct transcripts that can be detected at once27 and the number of cells that can be profiled at once28, given the appropriate equipment. Although smRNA FISH has its own limitations (potential false-positive and false-negative transcript detection, restrictions to only a few target genes per cell, the cost of equipment and reagents), it may provide a powerful method to complement and validate results from a qPCR-based analysis of a subset of target genes of interest.

Individual cells in a population can show widely differing responses to a uniform physiological stimulus1,2,3,4. The genetic variation of cells in a population is one mechanism for this variety of responses, but there are also several non-genetic factors that can increase the variability of responses, even in a clonal population of cells. For example, the levels of individual proteins and other important signaling molecules can vary on a cell-by-cell basis, giving rise to variation in downstream gene expression profiles. Additionally, gene activation can occur in short-duration bursts of transcripts5,6 that may be limited to a relatively small number of transcripts per burst7,8,9. Such stochasticity in gene activation can greatly contribute to variability in biological responses and can provide a selective advantage in microorganisms10 and in mammalian cells1,2 responding to a physiological stimulus. Due to both genetic and non-genetic sources of variation, the gene expression profile of any given cell in response to a stimulus may differ greatly from the average gene expression profile obtained from the measurement of the bulk response. Determining the extent to which individual cells show variability in response to a stimulus requires techniques for the isolation of individual cells, the measurement of the expression levels for transcripts of interest, and the computational analysis of the resulting expression data.
There are several approaches for assaying gene expression in single cells, covering a wide range of costs, number of transcripts probed, and accuracy of quantification. For example, single-cell RNA-Seq offers a wide depth of transcript coverage and the ability to quantify thousands of distinct transcripts for the most highly expressed genes in individual cells; however, the cost associated with such sequencing depth can be prohibitive, although costs continue to decrease. Conversely, single-molecule RNA fluorescence in situ hybridization (smRNA FISH) offers precise quantification of transcripts for even low-expressing genes at a reasonable cost per gene of interest; however, only a small number of target genes can be assayed in a given cell by this approach. Quantitative PCR-based assays, described in this protocol, provide a middle ground between these techniques. These assays employ a microfluidic real-time PCR machine to quantify up to 96 transcripts of interest at a time in up to 96 cells. While each of the aforementioned methods has requisite hardware costs, the cost of any individual qPCR assay is relatively low. This protocol is adapted from one suggested by a manufacturer of a microfluidic real-time PCR machine (Protocol ADP 41, Fluidigm). To enable the estimation of the absolute number of each transcript in a PCR-based approach, we have expanded the protocol to make use of internal controls of prepared target gene amplicons that can be used across multiple experiments.
As an example of this technique, the quantification of the expression of genes regulated by the tumor suppressor p53 in MCF-7 human breast carcinoma cells is described11. The cells are challenged with a chemical agent that induces DNA double-strand breaks. Previous studies have shown that the p53 response to DNA double-strand breaks exhibits a great deal of heterogeneity in individual cells, both in terms of p53 levels12 and in the activation of distinct target genes11. Furthermore, p53 regulates the expression of over 100 well-characterized target genes involved in numerous downstream pathways, including cell cycle arrest, apoptosis, and senescence13,14. Since the p53-mediated response in each cell is both complex and variable, the analysis of the system benefits from an approach in which nearly 100 target genes can be probed simultaneously in individual cells, such as that described below. With slight modifications (such as alternative methods for single-cell isolation and lysis), the protocol can be readily adapted to study a wide range of mammalian cell types, transcripts, and cellular responses.
With proper advance preparation, a round of cell sorting and gene expression measurement can be conducted according to this protocol over a period of three days. The following timing is suggested: in advance, select the transcripts of interest, identify and validate the primer pairs that amplify the cDNA from those transcripts, and prepare the standards and primer mixes using those primers. On Day 1, following cell treatment, harvest and sort the cells, perform reverse transcription and specific target amplification, and treat the samples with an exonuclease to remove unincorporated primers. On Day 2, perform quality control on sorted cells using qPCR. Finally, on Day 3, measure the gene expression in the sorted cells using microfluidic qPCR. Figure 1 summarizes the steps involved.

<table border="0" cellpadding="0" cellspacing="0" width="988">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:488px;">RNeasy Plus Mini Kit</td>
			<td style="width:187px;">Qiagen</td>
			<td style="width:147px;">74134</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">High Capacity cDNA Reverse Transcription Kit with RNase Inhibitor</td>
			<td>ThermoFisher</td>
			<td>4374966</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phusion High-Fidelity DNA Polymerase</td>
			<td>New England BioLabs</td>
			<td>M0530S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">QIAquick Gel Extraction Kit</td>
			<td>Qiagen</td>
			<td>28704</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Quant-iT High-Sensitivity dsDNA Assay Kit</td>
			<td>ThermoFisher</td>
			<td>Q33120&#160;</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2.0-mL low adhesion microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1420-2600</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">DNA Suspension Buffer</td>
			<td>Teknova</td>
			<td>T0221</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Axygen 0.2-mL Maxymum Recovery Thin Wall PCR Tubes</td>
			<td>Corning</td>
			<td>PCR-02-L-C</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">GE 96.96 Dynamic Array DNA Binding Dye Sample &#38; Assay Loading Reagent Kit</td>
			<td>Fluidigm</td>
			<td>100-3415</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone RPMI 1640 media</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30027.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fetal Bovine Serum, Certified (US)</td>
			<td>ThermoFisher</td>
			<td>16000-044</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Antibiotic-Antimycotic Solution</td>
			<td>Corning</td>
			<td>30-004-CI</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Neocarzinostatin</td>
			<td>Sigma</td>
			<td>N9162</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ELIMINase</td>
			<td>Decon Labs</td>
			<td>1101</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SUPERase-In</td>
			<td>ThermoFisher</td>
			<td>AM2696</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">CellsDirect One-Step qRT-PCR Kit</td>
			<td>ThermoFisher</td>
			<td>11753500</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">E. coli DNA</td>
			<td>Affymetrix</td>
			<td>14380 10 MG</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ThermalSeal Sealing Film, Sterile</td>
			<td>Excel Scientific</td>
			<td>STR-THER-PLT</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BD FACSAria IIu</td>
			<td>BD Biosciences</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone Trypsin 0.05%</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30236.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">PBS, 1x</td>
			<td>Corning</td>
			<td>21-040-CV</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Falcon 40&#181;m Cell Strainer</td>
			<td>Corning</td>
			<td>352340</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Exonuclease I</td>
			<td>New England BioLabs</td>
			<td>M0293S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SsoFast EvaGreen Supermix with Low ROX</td>
			<td>Bio-Rad</td>
			<td>172-5210</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">96.96 Dynamic Array IFC for Gene Expression (microfluidic qPCR chip)</td>
			<td>Fluidigm</td>
			<td>BMK-M-96.96</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">IFC Controller HX (loading machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BioMark or BioMark HD (microfluidic qPCR machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Real-Time PCR Analysis software&#160;</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MATLAB software</td>
			<td>MathWorks</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

single-cell gene expression profiling using facs and qpcr with internal standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

A general overview of the protocol is shown in Figure 1, including steps for cell treatment, the isolation of single cells by FACS, the generation and pre-amplification of cDNA libraries from single-cell lysates, the confirmation of single-cell cDNA libraries in sorted wells, and the measurement of gene expression by qPCR.
In preparation for single-cell isolation and gene expression analysis, it is necessary to first identify valid oligonucleotide primer pairs for each target gene of interest. Figure 2 shows two examples of primer quality-control methods. Melt curves are generated following qPCR with the primer pair being tested. Valid primers result in a melt curve with a single peak (Figure 2A, green curve). If multiple peaks (Figure 2A, blue curve) or a curve with a pronounced shoulder (Figure 2A, red curve) are present, this indicates the presence of multiple PCR products, and the primers should be redesigned. As a second quality-control method, agarose gel electrophoresis can be used to visually verify that a single band of the correct size is present for each primer pair tested (Figure 2B). Multiple bands or bands of the wrong size indicate that the primers are not valid (Figure 2B).
Following primer validation and the generation of libraries of qPCR standards from the correct amplicons, cell sorting can be performed. An example sorting scheme is indicated in Figure 3. Each sorted plate should include wells containing a dilution series of amplicon standards, with 10, 100, 1,000, or 10,000 copies of each target amplicon. It is also recommended to include wells into which no cells, 10 cells, or 100 cells are sorted, in addition to the single-cell wells. Due to sorting inefficiency, it is often necessary to identify the wells of the plate in which single cells have successfully been deposited. For this validation step, following the procedure for reverse transcription and specific target amplification, qPCR should be performed to measure the expression of a housekeeping gene (Figure 4). The wells for the amplicon standards (Figure 4, black curves) should show evenly spaced amplification curves. There should also be clear separation in the curves corresponding to the &#34;no-cell&#34; wells (Figure 4; red curves), &#34;10-cell&#34; wells (Figure 4; blue curves), and &#34;100-cell&#34; wells (Figure 4; purple curves). A clear separation should also occur for the &#34;1-cell&#34; wells corresponding to successful cell deposition (Figure 4; green amplification curves with Ct values less than those for the no-cell controls but greater than those for the 10-cell controls) versus unsuccessful cell deposition (Figure 4; green curves with Ct values near those for the no-cell controls). Once wells with a single cell lysate have been identified, cDNA from the wells (potentially from multiple plates) can be transferred to a microfluidic qPCR chip, with one single-cell lysate per well. For example, Figure 5 shows a hypothetical array that combines cDNA from single cells from three different sorted 96-well plates into a single new plate for qPCR analysis.
To analyze the microfluidic qPCR results, the samples that likely did not receive a cell should first be removed, as indicated by many missing or zero gene expression measurements (Figure 7, rows indicated by arrows) or unusually low housekeeping gene expression. For each assay, any measurements with melt peaks that do not match those of the other samples in the same assay should also be removed. After removing bad samples and measurements, linear regression can be performed on the measurements corresponding to the amplicon standards to estimate the absolute count of each transcript of interest in each single-cell sample. Figure 8 shows a visualization of these gene expression estimates, measured in units of molecules &#215; reverse transcription efficiency, as beeswarm and violin plots. In this example, untreated cells show a broad range of CDKN1A expression levels, with many cells not expressing CDKN1A at all and with others expressing the gene at levels spanning two orders of magnitude. After treatment with neocarzinostatin&#160;(NCS) for 3 hr, most cells express CDKN1A at much higher levels, and the variability decreases to roughly a single order of magnitude. As the duration of treatment increases, the average level of CDKN1A expression decreases and the variability in expression expands.
<img alt="Figure 1" src="/files/ftp_upload/55219/55219fig1.jpg" /> 
Figure 1: Overview of the steps in single-cell gene expression profiling. Treated cells are harvested and sorted via FACS directly into lysis buffer. mRNA species of interest are reverse-transcribed, and the resulting cDNA is amplified by PCR. Housekeeping gene expression in each sample is measured by qPCR to determine which samples actually received a cell. In samples passing this quality-control test, the expression of up to 96 genes is measured using microfluidic qPCR. This figure has been modified from a previous publication11. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55219/55219fig2.jpg" /> 
Figure 2: Example results of primer testing. (A) Melt curves from qPCR. The blue melt curve (TNFRSF10D) has multiple peaks and the red melt curve (TRIM22) has a &#34;shoulder,&#34; which amounts to a second peak close to the first peak. These indicate that the primers used in those reactions amplify multiple targets and need to be redesigned. The green melt curve (SCN3B) has a single peak, consistent with primers that amplify a single target. (B) Amplified DNA run on an agarose gel. The lanes for SCN3B, TRIM22, and TRPM2 contain multiple bands; the primers used to make those DNA amplicons amplify multiple targets and need to be redesigned. The lane for TNFRSF10D has a single band, consistent with primers that amplify a single target. It is worth noting that the primers for SCN3B pass the melt curve test but fail the gel test, while those for TNFRSF10D pass the gel test but fail the melt curve test; these two methods of primer validation are complementary. In this example, one can conclude that all four primer sets need to be redesigned. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55219/55219fig3.jpg" /> 
Figure 3: Plate map for cell sorting. This plate map includes 1-cell wells (red), 10- and 100-cell wells (darker red), standards (green), and no-cell wells (white). Including two replicates of each standard on each plate helps to compensate for variability in standards. Including 10-cell and 100-cell wells is helpful as a sanity check for gene expression. The no-cell wells serve as a negative control. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55219/55219fig4.jpg" /> 
Figure 4: Example of qPCR amplification curves from sort quality control measuring GAPDH expression. No-cell samples (red) show a background level of expression. 1-cell samples (green) divide into two distinct groups, one with high expression and one with low expression similar to the no-cell samples. The high-expression samples most likely received a real cell during sorting; the low-expression samples most likely did not. 10-cell (blue) and 100-cell (purple) samples show higher expression than the 1-cell samples; the 10-cell samples are close to the highest-expressing 1-cell samples because of sort inefficiency. Standards (black) are evenly distributed, as expected. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/55219/55219fig5.jpg" /> 
Figure 5: Example of Sample Mixes plate map. This plate map includes 1-cell samples from three different plates (rows A-G), standards mixed from all three plates (H1-H8), no-cell controls (H9-H10), and no-template controls for qPCR (H11-H12). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/55219/55219fig6.jpg" /> 
Figure 6: Diagram of microfluidic qPCR chip with the order of loading (1, 2, 3&#8230;). The notch (A1) is the top left corner of the plate. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/55219/55219fig7.jpg" /> 
Figure 7: Heat map of the qPCR results. Each row represents a 1-cell sample, standard, or control; each column represents a measured transcript. Rows with an unusual number of black squares or Xs (arrows) likely indicate samples that did not receive an actual cell. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" src="/files/ftp_upload/55219/55219fig8.jpg" /> 
Figure 8: Beeswarm and violin plots. In a beeswarm plot (blue points), each point represents gene expression in a particular cell. In a violin plot (gray shape), the width of the &#34;violin&#34; represents the frequency of gene expression measurements around a particular level in the population of cells analyzed. Light blue bars represent the lowest standard. Measurements above the blue bars are interpolated between standards, while measurements within the blue bars are extrapolated. The data shown here are measurements of CDKN1A expression in MCF-7 cells expressing Venus-tagged p53 treated with neocarzinostatin (NCS) for the indicated times in order to induce DNA double-strand breaks. This figure has been modified from previous publication11. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55219/55219fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Condition</td>
			<td>Temperature</td>
			<td>Time</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>50 &#176;C</td>
			<td>15 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>95 &#176;C</td>
			<td>2 min</td>
		</tr>
		<tr>
			<td rowspan="2">20 cycles</td>
			<td>95 &#176;C</td>
			<td>15 sec</td>
		</tr>
		<tr>
			<td>60 &#176;C</td>
			<td>4 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>4 &#176;C</td>
			<td>&#8734;</td>
		</tr>
	</tbody>
</table>
Table 1: Reverse transcription and specific target amplification (RTSTA) thermal cycling program.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Condition</td>
			<td>Temperature</td>
			<td>Time</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>37 &#176;C</td>
			<td>30 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>80 &#176;C</td>
			<td>15 min</td>
		</tr>
		<tr>
			<td>Hold</td>
			<td>4 &#176;C</td>
			<td>&#8734;</td>
		</tr>
	</tbody>
</table>
Table 2: Exonuclease I treatment (EXOI) thermal cycling program.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Volume/well (&#181;l)</td>
			<td>96 volumes w/ 10% overage (&#181;l)</td>
		</tr>
		<tr>
			<td>2x reaction mix</td>
			<td>5.00</td>
			<td>528.00</td>
		</tr>
		<tr>
			<td>Reverse transcriptase/polymerase mix</td>
			<td>0.20</td>
			<td>21.12</td>
		</tr>
		<tr>
			<td>10x STA Primer Mix</td>
			<td>1.00</td>
			<td>105.60</td>
		</tr>
		<tr>
			<td>2.64 U/&#181;l (dilute) RNAse inhibitor</td>
			<td>0.02</td>
			<td>2.00</td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>0.78</td>
			<td>82.48</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>7.00</td>
			<td>739.20</td>
		</tr>
	</tbody>
</table>
Table 3: Components of the lysis buffer for the cells.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Component</td>
			<td>Volume/well (&#181;l)</td>
			<td>96 volumes w/ 10% overage (&#181;l)</td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>2.52</td>
			<td>266.00</td>
		</tr>
		<tr>
			<td>Exonuclease I Reaction Buffer (10x)</td>
			<td>0.36</td>
			<td>38.00</td>
		</tr>
		<tr>
			<td>Exonuclease I (20 U/&#181;l)</td>
			<td>0.72</td>
			<td>76.00</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>3.60</td>
			<td>380.00</td>
		</tr>
	</tbody>
</table>
Table 4: Components of the exonuclease mix to treat the amplified cDNA samples.

Watch this Scientific Journal Video about Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards at JoVE.com

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards | Genetics | Video

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

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16.3K Views.  National Cancer Institute. We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Video: Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental changes. Considerable progress in our understanding of the tight connection between the circadian clock and most aspects of physiology has been made in the field over the last decade. However, unraveling the molecular basis that underlies the function of the circadian oscillator in humans stays of highest technical challenge. Here, we provide a detailed description of an experimental approach for long-term (2-5 days) bioluminescence recording and outflow medium collection in cultured human primary cells. For this purpose, we have transduced primary cells with a lentiviral luciferase reporter that is under control of a core clock gene promoter, which allows for the parallel assessment of hormone secretion and circadian bioluminescence. Furthermore, we describe the conditions for disrupting the circadian clock in primary human cells by transfecting siRNA targeting CLOCK. Our results on the circadian regulation of insulin secretion by human pancreatic islets, and myokine secretion by human skeletal muscle cells, are presented here to illustrate the application of this methodology. These settings can be used to study the molecular makeup of human peripheral clocks and to analyze their functional impact on primary cells under physiological or pathophysiological conditions.

Here, we describe settings to monitor in parallel circadian bioluminescence and the secretory activity of human islet cells and primary myotubes. For this, we employed lentiviral gene delivery of a luciferase core clock reporter, followed by in vitro synchronization and collection of outflow medium by continuous cell perifusion.

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Detection of microRNA Expression in Peritoneal Membrane of Rats Using Quantitative Real-time PCR

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been investigated in various organs and tissues in rat. However, standard methods for the purification of miRNAs and detection of their expression in rat peritoneal membrane have not been well established. We have developed an effective and reliable method to purify and quantify miRNAs using quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) in rat peritoneal membrane. This protocol consists of four steps: 1) purification of peritoneal membrane sample; 2) purification of total RNA including miRNA from peritoneal membrane sample; 3) reverse transcription of miRNA to produce cDNA; and 4) qRT-PCR to detect miRNA expression. Using this protocol, we successfully determined that the expression of six miRNAs (miRNA-142-3p, miRNA-21-5p, miRNA-221-3p, miRNA-223-3p, miRNA-327, and miRNA-34a-5p) increased significantly in the peritoneal membrane of a rat peritoneal fibrosis model compared with those in control groups. This protocol can be used to study the profile of miRNA expression in the peritoneal membrane of rats in many pathological conditions.

Here we present a protocol for the detection of microRNA expression in rat peritoneal membrane using quantitative real-time reverse-transcription polymerase chain reaction. This method is suitable for studying the microRNA expression profile in rat peritoneal membrane in several pathological conditions.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been ...

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such as cancer. Cell cycle-regulated gene expression analysis depends on cell synchronization into specific phases. Here we describe a method utilizing two complementary synchronization protocols that is commonly used for studying periodic variation of gene expression during the cell cycle. Both procedures are based on transiently blocking the cell cycle in one defined point. The synchronization protocol by hydroxyurea (HU) treatment leads to cellular arrest in late G1/early S phase, and release from HU-mediated arrest provides a cellular population uniformly progressing through S and G2/M. The synchronization protocol by thymidine and nocodazole (Thy-Noc) treatment blocks cells in early mitosis, and release from Thy-Noc mediated arrest provides a synchronized cellular population suitable for G1 phase and S phase-entry studies. Application of both procedures requires monitoring of the cell cycle distribution profiles, which is typically performed after propidium iodide (PI) staining of the cells and flow cytometry-mediated analysis of DNA content. We show that the combined use of two synchronization protocols is a robust approach to clearly determine the transcriptional profiles of genes that are differentially regulated in the cell cycle (i.e. E2F1 and E2F7), and consequently to have a better understanding of their role in cell cycle processes. Furthermore, we show that this approach is useful for the study of mechanisms underlying drug-based therapies (i.e. mitomycin C, an anticancer agent), because it allows to discriminate genes that are responsive to the genotoxic agent from those solely affected by cell cycle perturbations imposed by the agent.

We report two cell synchronization protocols that provide a context for studying events related to specific phases of the cell cycle. We show that this approach is useful for analyzing the regulation of specific genes in an unperturbed cell cycle or upon exposure to agents affecting the cell cycle.

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in which the gene of interest has been disrupted. A gene-disruption DNA construct containing a suitable antibiotic resistance marker gene is useful for the generation of gene-disrupted strains in bacteria. However, conventional construction methods, which require gene cloning steps, involve complex and time-consuming protocols. Here, a relatively facile, rapid, and cost-effective method for targeted gene disruption in Streptococcus mutans is described. The method utilizes a 2-step fusion polymerase chain reaction (PCR) to generate the disruption construct and electroporation for genetic transformation. This method does not require an enzymatic reaction, other than PCR, and additionally offers greater flexibility in terms of the design of the disruption construct. Employment of electroporation facilitates the preparation of competent cells and improves the transformation efficiency. The present method may be adapted for the generation of gene-disrupted strains of various species.

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

We describe a facile, rapid, and relatively inexpensive method for the generation of gene-disrupted Streptococcus mutans strains; this technique may be adapted for the generation of gene-disrupted strains of various species.

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in ...

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc2155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

We describe an original transfer RNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). SPOt simultaneously measures cellular levels of all tRNAs in biological samples, in only three steps, and in less than 24 hours.

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for ...